Abstract
This article aims to assess the effect of embodied interaction on attention during the process of solving spatio-visual navigation problems. It presents a method that links operator's physical interaction, feedback, and attention. Attention is inferred through networks called Bayesian Attentional Networks (BANs). BANs are structures that describe cause-effect relationship between attention and physical action. Then, a utility function is used to determine the best combination of interaction modalities and feedback. Experiments involving five physical interaction modalities (vision-based gesture interaction, glove-based gesture interaction, speech, feet, and body stance) and two feedback modalities (visual and sound) are described. The main findings are: (i) physical expressions have an effect in the quality of the solutions to spatial navigation problems; (ii) the combination of feet gestures with visual feedback provides the best task performance.
| Original language | English |
|---|---|
| Article number | 3 |
| Journal | ACM Transactions on Interactive Intelligent Systems |
| Volume | 7 |
| Issue number | 1 |
| DOIs | |
| State | Published - 1 Dec 2016 |
| Externally published | Yes |
Keywords
- Bayesian network
- Embodied interaction
- attention inference
- gesture interaction
- multimodal interaction
ASJC Scopus subject areas
- Human-Computer Interaction
- Artificial Intelligence